Accepted Posters
Category 'X'- Systems Biology and Networks' |
Poster X001 |
Dynamic Deterministic Effects Propagation Networks for learning signalling pathways from reverse phase protein array time series data |
Christian Bender- German Cancer Research Center |
Frauke Henjes (German Cancer Research Center, Molecular Genome Anlaysis); Holger Fröhlich (Bonn-Aachen International Centre for IT, Department of Algorithmic Bioinformatics); Stefan Wiemann (German Cancer Research Center, Molecular Genome Analysis); Ulrike Korf (German Cancer Research Center, Molecular Genome Analysis); Tim Beißbarth (University of Göttingen, Medical Statistics (Biostatistics/Bioinformatics)); |
Short Abstract: To understand signalling behaviour under external perturbation in human breast cancer cell lines we develop a method for reconstruction of signalling networks from Reverse-Phase-Protein-Array time course data. A discrete signal propagation scheme is combined with a Hidden-Markov-Model to model dynamics, and network structure search is done by a genetic algorithm. |
Long Abstract:Click Here |
|
Poster X003 |
An algorithm for smart oligo pooling to construct synthetic gene library |
Shanrong Zhao- Johnson & Johnson |
Jose Pardinas (Centocor Discovery Research, Synthetic Gene Engineering); |
Short Abstract: A novel algorithm has been developed that makes possible the simultaneous generation of multiple homologous genes during the same oligonucleotide-based gene assembly process without production of extraneous undesirable side products. |
Long Abstract:Click Here |
|
Poster X004 |
Uncertainty Quantification and Minimization in Gene Expression Data |
Lee Falin- Virginia Bioinformatics Institute, Virginia Polytechnic and State University, Blacksburg, Virginia |
No additional authors |
Short Abstract: Uncertainty quantification and analysis is a topic of major interest in many fields that use mathematical models trained by sparse and noisy measurements
We have developed a novel method of uncertainty quantification and minimization for gene expression data that seeks to guide researchers in designing new experiments from preliminary data. |
Long Abstract:Click Here |
|
Poster X005 |
The Function of Communities in Protein Interaction Networks at multiple scales |
Anna Lewis- University of Oxford |
Charlotte Deane (University of Oxford, Statistics); Nick Jones (University of Oxford, Physics); Mason Porter (Unversity of Oxford, Mathematics); |
Short Abstract: We investigate the link between biological modules and protein interaction network communities at multiple scales, or resolutions, in yeast. We show that there is no one scale of interest, but we can identify the range of resolution parameters that yield the most functionally coherent communities. |
Long Abstract:Click Here |
|
Poster X006 |
Hepatic lobular model of metabolism at individual cellular level |
Ayako Yachie-Kinoshita- School of Medicine, Keio University |
Hideo Yokota (RIKEN, Computational Science Research Program); Makoto Suematsu (School of Medicine, Keio University, Department of Biochemistry & Integrative Medical Biology); |
Short Abstract: Based on the metabolic model of hepatocytes including over 500 differential equations, hepatic louble model has been built up using Riken Integrated Cell Simulator (RICS) based on the zone-specific hepatocyte model and the spatial layout of sinusoid, the space of disse and hepatocytes considering the heterogeneity of metabolism. |
Long Abstract:Click Here |
|
Poster X007 |
Non-linear predictive modeling of toxicological metabonomics data using simulated annealing optimized kernel-OPLS |
Judith Fonville- Imperial College London |
Mattias Rantalainen (University of Oxford, Department of Statistics); Muireann Coen (Imperial College London, Biomolecular Medicine); Jeremy Nicholson (Imperial College London, Biomolecular Medicine); Elaine Holmes (Imperial College London, Biomolecular Medicine); |
Short Abstract: Many biological datasets display significant non-linearity, resulting in sub-optimal prediction performance using conventional linear models. We present an extension to the Kernel-based Orthogonal Projection to Latent Structures model that incorporates Simulated Annealing for automatic optimization of the kernel parameter. We demonstrate improved classification and time-response prediction of toxicological metabolic data. |
Long Abstract:Click Here |
|
Poster X008 |
Modelling of protein-protein interactions network in parasite genomes |
Antonio Rezende- Rene Rachou Research Center |
Artur Silva (Federal University of Para, Genetic Department); Vasco Azevedo (Federal University of Minas Gerais, Genetic Department); Jeronimo Ruiz (Rene Rachou Research Center, Cellular and Molecular Parasitology Laboratory); |
Short Abstract: In this work we model a protein-protein interaction (PPI) network for Corynebacterium pseudotuberculosis using three different computational methodologies. Predicted by at least two methodologies we found 1189, 1197, 1206, 1182 and 1195 pairs of protein interactions for each studied strain of C. pseudotuberculosis (Cp1002, CpC231, CpCIP5297, CpI-19 and CpPAT10) respectively. |
Long Abstract:Click Here |
|
Poster X009 |
A Systems Approach to Understanding Embryonic Stem Cell Self Renewal Mechanisms |
Karen Dowell- University of Maine |
Matthew A. Hibbs (The Jackson Laboratory, Hibbs Laboratory); |
Short Abstract: The molecular mechanisms involved in stem cell self renewal are elaborate and elusive. We present a Bayesian network machine-learning approach for conducting an extensive systems analysis of mouse embryonic stem cell self renewal pathways, and introduce a public resource for data analysis and visualization of predicated protein functional relationships. |
Long Abstract:Click Here |
|
Poster X010 |
Towards More Fine and Robust Stochastic Gene Expression Modeling with Hill Functions of Protein Degradation |
Haseong Kim- Imperial College London |
Gelenbe Erol (Imperial College London, Department of Electrical and Electronic Engineering); |
Short Abstract: We introduce a new stochastic gene expression modeling approach. Our model includes eight gene expression relevant processes. Also Hill type functions are applied to the Gillespie algorithm which simulates our expression model. Our toggling switch model successfully produces oscillatory expression profiles without abnormal expression behaviors which is commonly shown in conventional stochastic models. |
Long Abstract:Click Here |
|
Poster X011 |
TRANSCRIPTIONAL REGULATORY SUBNETWORKS IN THE MOUSE BRAIN AS DERIVED FROM DATA SETS IN THE ALLEN MOUSE BRAIN ATLAS |
Ronald Taylor- Pacific Northwest National Laboratory |
George Acquaah-Mensah (Massachusetts College of Pharmacy and Health Sciences, Pharmaceutical Sciences); Jason McDermott (Pacific Northwest National Laboratory, Computational Biology & Bioinformatics Group); |
Short Abstract: The gene expression measurements in the mouse brain atlas at the Allen Institute form a unique resource. We present preliminary results using algorithms that employ correlations in gene state to infer gene-to-gene regulation on such Allen data for the determination of high-confidence transcriptional regulatory subnetworks in the mouse brain. |
Long Abstract:Click Here |
|
Poster X012 |
Integrative analysis of time course microarray data and DNA sequence data via log-linear models for identifying dynamic transcriptional regulatory networks |
Hyung-Seok Choi- Ewha Research Center for Systems Biology |
Youngchul Kim (Seoul National University, Department of Statistics); Kwang-Hyun Cho (Korea Advanced Institute of Science and Technology, Department of Bio and Brain Engineering and KI for the BioCentury); Wan Kyu Kim (Ewah Womans University, Division of Life and Pharmaceutical Sciences); Sanghyuk Lee (Ewah Womans University, Division of Life and Pharmaceutical Sciences); Taesung Park (Seoul National University, Division of Life and Department of Statistics); |
Short Abstract: We propose a method based on dynamic cis-regulatory elements. Our methods combine time course microarray data, information on physical binding between the transcription factors (TFs) and their targets, and the regulatory sequences of genes. A log-linear model was developed to predict combination sets of TFs from integrated data. |
Long Abstract:Click Here |
|
Poster X013 |
Application of numerical algorithms to mimic cancer as a ‘systems disease’ reveals novel cancer genes |
SHIVASHANKAR HIRIYUR NAGARAJ- CSIRO |
No additional authors |
Short Abstract: Recognizing cancer as a 'systems disease', we introduce a novel approach to study cancer that faithfully mimics 'cancer characteristics' and effectively predicts novel cancer genes. Using a test dataset, we demonstrate the optimal balance of biology and numerical algorithms in this method to allow for a holistic overview of cancer. |
Long Abstract:Click Here |
|
Poster X014 |
Integrated network from phylogenetic profiling discovers genomic characteristics in functional linkages between high eukaryote genes |
Junha Shin- College of Life science and Biotechnology, Yonsei University |
Insuk Lee (College of Life science and Biotechnology, Yonsei University, Biotechnology); |
Short Abstract: Phylogenetic profiling has been used for reconstruction of gene networks, but application for higher eukaryotes was suggested that limited. We revisited the method with 1,056 genomes and found several optimal conditions. Profiles based on three genome-set infers very different categories of functional linkages, which implicates differential enrichment of pathways. |
Long Abstract:Click Here |
|
Poster X015 |
Prediction of Functional Gene Associations Using Domain Co-occurrence Approach |
Jung Eun Shim- Yonsei Univ. |
Insuk Lee (Yonsei Univ., Department of Biotechnology); |
Short Abstract: Gene functional association prediction approaches employing protein domain knowledge can predict global view of pathway organization and function of proteins. However, the most of domain-based approaches require reference protein-protein interactions. This study proposes a domain co-occurrence method based on weighted mutual information using only protein domain information. |
Long Abstract:Click Here |
|
Poster X016 |
Efficient query-driven biclustering of gene expression data using Probabilistic Relational Models |
Lore Cloots- K.U.Leuven |
Hui Zhao (K.U.Leuven, CMPG); Tim Van den Bulcke (K.U.Leuven, ESAT); Riet De Smet (K.U.Leuven, CMPG); Yan Wu (K.U.Leuven, CMPG); Kristof Engelen (K.U.Leuven, CMPG); Bart De Moor (K.U.Leuven, ESAT); Kathleen Marchal (K.U.Leuven, CMPG); |
Short Abstract: We present a novel method ProBic, which identifies overlapping biclusters in gene expression data by performing directed queries around genes of interest. ProBic identifies biologically sound biclusters and, bicluster identification is robust as the set of query genes can contain genes that are not part of the bicluster of interest. |
Long Abstract:Click Here |
|
Poster X017 |
Systematic analysis of mitochondrial proteins involved in the communication to the cell |
Sanguk Kim- POSTECH |
Jae-Seong Yang (POSTECH, School of Interdisciplinary Bioscience and Bioengineering); Jouhyun Jeon (POSTECH, Division of Molecular and Life Science); Solip Park (POSTECH, School of Interdisciplinary Bioscience and Bioengineering); Yun-joo Yoo (POSTECH, School of Interdisciplinary Bioscience and Bioengineering); Jinho Kim (POSTECH, Division of Molecular and Life Science); Youngeun Shin (POSTECH, Division of Molecular and Life Science); |
Short Abstract: None On File |
Long Abstract:Click Here |
|
Poster X018 |
Burkitts Lymphoma and Malaria: A Study of Probable Interactions |
Edward Kensah- Massachusetts College of Pharmacy & Health Sciences |
Karen Duca (Kwame Nkrumah University of Science and Technology, Biochemistry and Biotech); George Acquaah-Mensah (Massachusetts College of Pharmacy & Health Sciences, Pharmaceutical Sciences); |
Short Abstract: Building on the observed association between Burkitt's Lymphoma (BL) and malaria, a goal was to identify interactions involving invading P. falciparum proteins and those of the host human B cell. Dependencies, clusters, and interactions involving BL-relevant molecules including MYC and AID were identified, elucidating probable interactions in BL. |
Long Abstract:Click Here |
|
Poster X019 |
miRTar: A web server for identifying microRNA targets in humans |
Justin Bo-Kai Hsu- National Chiao Tung University |
Sheng-Da Hsu (National Chiao Tung University, Bioinformatics and Systems Biology); Chih-Min Chiu (National Chiao Tung University, Bioinformatics and Systems Biology); Tzong-Yi Lee (Yuan Ze University, Computer Science and Engineering); Hsien-Da Huang (National Chiao Tung University, Biological Science and Technology); |
Short Abstract: miRTar is a tool for identifying the regulated relationship between a group of known/putative miRNAs and protein coding genes also it can provide another viewpoint about the miRNA targets on alternatively spliced transcripts. |
Long Abstract:Click Here |
|
Poster X020 |
Correction for ascertainment bias in literature-curated interaction networks |
Jonathan Dickerson- University of Manchester |
David Robertson (University of Manchester, Faculty of Life Sciences); John Pinney (Imperial College London, Centre for Bioinformatics); |
Short Abstract: Network biology is providing us with a toolkit to represent and understand biological information. However, robust analyses are reliant upon accurate underlying data to draw conclusions. Literature-curated datasets, however, contain significant bias that has the potential to undermine results. Here we present a novel methodology to correct for such bias. |
Long Abstract:Click Here |
|
Poster X021 |
Edge Clipper: an algorithm for Bayesian consensus network refinement and its application in microarray-based pathway analysis |
Andrew Hodges- University of Michigan |
Peter Woolf (University of Michigan, Center for Computational Medicine and Bioinformatics, Department of Chemical Engineering, Department of Biomedical Engineering); Yongqun He (University of Michigan, Center for Computational Medicine and Bioinformatics, Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology); |
Short Abstract: An "Edge Clipper" algorithm was developed for Bayesian consensus network refinement and applied to analyzing gene regulatory pathways. Studies based on E.coli pathways and gene expression data demonstrated that Edge Clipper can improve specificity of consensus networks while sacrificing sensitivity, and can be applied to reconstructing other biological pathways. |
Long Abstract:Click Here |
|
Poster X022 |
Clusters of innovation in the mammalian metabolic network |
Michaël Bekaert- University of Missouri |
Gavin Conant (Universiry of Missouri, Animal Sciences Center); |
Short Abstract: Using the human metabolic network, we inferred networks in other mammals. For each genome, we considered the orthologous genes that correspond to the enzyme-coding genes from humans. We searched the network for duplication-enriched clusters, using the connected network component of enzyme-centered view of the metabolic networks. |
Long Abstract:Click Here |
|
Poster X023 |
Exploring the Monochromatic Landscape in Yeast using Genetic Interactions and Known Processes Reveals the Importance of Protein Complexes |
Magali Michaut- University of Toronto |
Anastasia Baryshnikova (University of Toronto, CCBR); Michael Costanzo (University of Toronto, CCBR); Chad L Myers (University of Minnesota, Department of Computer Science and Engineering); Brenda Andrews (University of Toronto, CCBR); Charlie Boone (University of Toronto, CCBR); Gary D Bader (University of Toronto, CCBR); |
Short Abstract: Monochromaticity refers to the ratio of positive to negative genetic interactions within a set of genes and indicates the functional coherence of biological systems. We use quantitative genetic interactions in budding yeast to define a monochromatic map of biological processes and show that protein complexes are important in this map. |
Long Abstract:Click Here |
|
Poster X024 |
The human E3 ubiquitin ligase enzyme protein interaction network |
Gozde Kar- Koc University |
Ozlem Keskin (Koc University, Center for Computational Biology and Bioinformatics); Ruth Nussinov (Basic Research Program, SAIC-Frederick, Center for Cancer Research Nanobiology Program, NCI-Frederick); Attila Gursoy (Koc University, Center for Computational Biology and Bioinformatics); |
Short Abstract: Here, using biological data, structural information, high performance structural comparison algorithms and appropriate filters, we construct the human E3 ubiquitin ligase enzyme protein interaction network. Analysis of the network reveals important functional features and uncovers an a priori unknown E3-E2 and E3-E3 interactions. |
Long Abstract:Click Here |
|
Poster X025 |
Dual Role of miRNAs in NF-kB signaling pathway |
Candida Vaz- Jawaharlal Nehru University |
Arvind Singh Mer (Jawaharlal Nehru University, Centre for Computational Biology and Bioinformatics; School of Information Technology); Ramakrishna Ramaswamy (Jawaharlal Nehru University, Centre for Computational Biology and Bioinformatics; School of Information Technology and School of Physical Sciences); Alok Bhattacharya (Jawaharlal Nehru University, Centre for Computational Biology and Bioinformatics; School of Information Technology and School of Life Sciences); |
Short Abstract: Our Computational study of the effect of miRNA on the dynamics of NF-kB pathway has provided fresh insights into how the cell regulates inflammatory responses to signals,increasing or decreasing it as need be, through miRNAs. We suggest that miRNAs could have a dual role in fine- tuning the NF-kB dynamics. |
Long Abstract:Click Here |
|
Poster X026 |
Structural models predict changes of SH3 domain mediated protein interactions in yeast species |
erik verschueren- CRG - Center for Genomic Regulation |
Francois Stricher (CRG - Center for Genomic Regulation, Systems Biology unit); Luis Serrano (CRG - Center for Genomic Regulation, Systems Biology unit); Peter Vanhee (VIB - Flanders Institute of Biotechnology / VUB - Free University of Brussels, SWITCH laboratory); Joost Schymkowitz (VIB - Flanders Institute of Biotechnology / VUB - Free University of Brussels, SWITCH laboratory); |
Short Abstract: Comparing protein interaction networks between species helps us understand how organisms evolved. Here we compare protein interactions mediated by SH3 domains across four yeast species. We show how structural models can help to identify true interactions and are of great use to predict protein interaction networks in less characterized organisms. |
Long Abstract:Click Here |
|
Poster X027 |
Mathematical modeling of preserved red blood cells towards improvement of blood storage |
Taiko Nishino- Keio University |
Ayako Yachie-Kinoshita (Keio University, Biochemistry and Integrative Medical Biology, School of Medicine); Akiyoshi Hirayama (Keio University, Institute for Advanced Biosciences); Tomoyoshi Soga (Keio University, Institute for Advanced Biosciences); Makoto Suematsu (Keio University, Biochemistry and Integrative Medical Biology, School of Medicine); Masaru Tomita (Keio University, Institute for Advanced Biosciences); |
Short Abstract: We developed a mathematical model of metabolism in cold-stored erythrocytes. The model successfully reproduced time-series metabolomics data collected by CE-TOFMS analysis. This work indicates the usability of the model to propose novel methods for cold blood preservation. |
Long Abstract:Click Here |
|
Poster X028 |
Identification of essential genes associated with aging using correlation networks |
Kathryn Dempsey- University of Nebraska Medical Center |
Stephen Bonasera (University of Nebraska Medical Center, Internal Medicine - Geriatrics); Dhundy Bastola (University of Nebraska at Omaha, Information Science and Technology); Hesham Ali (University of Nebraska at Omaha, College of Information Science and Technology); |
Short Abstract: We propose a correlation network model to capture relationships among expression data. Networks for young, middle-aged, and aged mice are obtained and causative structures such as hubs, pathways and clusters are identified with graph theoretic approaches. We found that knockout of 70% of highly-connected "hub" genes resulted in mouse lethality. |
Long Abstract:Click Here |
|
Poster X029 |
Module Detection in Complex Networks |
Laura Bennett- Kings College London |
No additional authors |
Short Abstract: The modularity metric expresses topological properties of networks. Several methodologies for community structure detection based on modularity maximisation have been developed. We present a mathematical programming approach using mixed integer nonlinear programming models for community detection that tackles weighted networks and networks with nodes belonging to multiple modules. |
Long Abstract:Click Here |
|
Poster X030 |
simplyGO: a Cytoscape plug-in for quick and easy graph visualization and functional analyses |
Alberto Paccanaro- Royal Holloway University of London |
Ahmed Hassan (Royal Holloway University of London, Department of Computer Sciences); Prajwal Bhat (Royal Holloway University of London, Department of Computer Sciences); Tamas Nepusz (Royal Holloway University of London, Department of Computer Sciences); Haixuan Yang (Royal Holloway University of London, Department of Computer Sciences); |
Short Abstract: implyGO is a simple, intuitive, user friendly tool which allows the user to quickly visualize a biological network and integrate it with GO information by colouring the nodes according to their GO annotation and performing over-representation analysis. |
Long Abstract:Click Here |
|
Poster X031 |
Comparison of different null hypotheses to identify differentially expressed pathways |
Shailesh Tripathi- Queens University, Belfast |
Frank Emmert-Streib (Computational Biology and Machine Learning, Queen's University Belfast); |
Short Abstract: Many test statistics and various null hypotheses have been suggested analyzing groups of genes to detect pathological pathways. Here we extend our previous method by investigating the influence of different null hypotheses on the comparison treatment vs control groups. We test our method with simulated and microarray data from ALL. |
Long Abstract:Click Here |
|
Poster X032 |
Network biomarkers of C. elegans aging |
Kristen Fortney- University of Toronto |
Max Kotlyar (University of Toronto, Medical Biophysics); Igor Jurisica (University of Toronto, Medical Biophysics); |
Short Abstract: A central goal of biogerontology is to identify robust gene-expression biomarkers of aging. We develop a method where the biomarkers are networks of genes selected based on age-dependent activity and a graph-theoretic property called modularity. We apply these subnetwork biomarkers to assign novel aging-related functions to poorly characterized longevity genes. |
Long Abstract:Click Here |
|
Poster X033 |
Model SEED: a resource for high-throughput generation, optimization, and analysis of genome-scale metabolic models |
Matt DeJongh- Hope College |
Christopher Henry (Argonne National Laboratory, Mathematics and Computer Science); Aaron Best (Hope College, Biology); Paul Frybarger (Hope College, Biology); Ben Linsay (University of Chicago, Computer Science); Rick Stevens (University of Chicago, Computer Science); |
Short Abstract: The Model SEED (www.theseed.org/models) generates draft genome-scale metabolic models from unannotated genome sequences in 48 hours. We have generated 130 models representing diverse prokaryotes, and predicted reaction flux, nutrient requirements, and gene essentiality in every model. We optimized 22 models using experimental data, boosting accuracy from 66% to 87%. |
Long Abstract:Click Here |
|
Poster X034 |
Functional implications of tissue-specific gene expression using RNA-sequencing data |
Dorothea Emig- Max Planck Institute for Informatics |
Mario Albrecht (Max Planck Institute for Informatics , Computational Biology and Applied Algorithmics); |
Short Abstract: RNA-sequencing allows for accurately measuring gene expression, which has recently revealed that more universally-expressed genes exist than thought previously. However, the functional implications thereof have not been investigated yet. We integrated RNA-sequencing data with proteins, domains, and their interactions to identify functions relevant for tissue specificity. |
Long Abstract:Click Here |
|
Poster X035 |
Mathematical modeling of the gene regulatory network controlling the cold shock response in Saccharomyces cerevisiae |
Kam Dahlquist- Loyola Marymount University |
Alondra Vega (Loyola Marymount University, Mathematics); Stephanie Kuelbs (Loyola Marymount University, Mathematics); Ben Fitzpatrick (Loyola Marymount University, Mathematics); |
Short Abstract: Using differential equations, we modeled the dynamics of a transcriptional network of fifteen genes controlling the response to cold shock in yeast. We determined parameter values using microarray data. Model/data comparisons were performed with least squares. Model predictions fit the data well within two standard deviations of the experimental error. |
Long Abstract:Click Here |
|
Poster X036 |
Systematic modeling study on mechanism of p38 MAPK activation in MDS |
Xiaobo Zhou- The Methodist Hospital Research Institute |
Huiming Peng (The Methodist Hospital Research Institute, Radiology); Jianguo Wen (The Methodist Hospital Research Institute, Pathology); Hongwei Li (China University of Geosciences, Mathematics and Physics); Jeff Chang (The Methodist Hospital Research Institute, Pathology); |
Short Abstract: We propose a novel pathway modeling methodology to systematically explore the potential mechanism of p38 activation in Myelodysplastic syndromes (MDS) by combining mathematical model with time-series RPPA data. The results suggest the proposed method may serve as a powerful exploration tool for predicting biological hypotheses on study of MDS pathogenesis. |
Long Abstract:Click Here |
|
Poster X037 |
Understanding the Signaling Kinetics that Drive T Cell Biasing During Dendritic Cell Maturation |
Joanna González-Lergier- Mount Sinai School of Medicine |
Jeremy Seto (Mount Sinai School of Medicine, Neurology); Sonali Patil (Mount Sinai School of Medicine, Neurology); Stuart Sealfon (Mount Sinai School of Medicine, Neurology); |
Short Abstract: Dendritic cells recognize pathogens and subsequently undergo a maturation process, including the production of a cytokines which stimulate the appropriate response from other immune cells. Therefore, we are modeling the signaling that occurs from pathogen detection to the production of different cytokines which are associated with distinct T cell responses. |
Long Abstract:Click Here |
|
Poster X038 |
Inferring physical protein contacts in protein complex purifications |
Sven-Eric Schelhorn- Max Planck Institute for Informatics |
Julián Mestre (Max Planck Institute for Informatics, Algorithms and Complexity); Mario Albrecht (Max Planck Institute for Informatics, Computational Biology and Applied Algorithmics); Elena Zotenko (Max Planck Institute for Informatics, Computational Biology and Applied Algorithmics); |
Short Abstract: We show that raw protein complex purification data can be exploited to discover high-confidence physical contacts between proteins in complexes with accuracy comparably to binary measurement techniques. Our new scoring method outperforms other approaches at detecting physical contacts involving proteins that have been screened multiple times in purification experiments. |
Long Abstract:Click Here |
|
Poster X039 |
MP Editor : visualization, integration and functional analysis of signaling pathways using molecule pages |
Ashok Reddy Dinasarapu- University of California San Diego |
No additional authors |
Short Abstract: Molecule Page (MP) Editor is a java based tool for pathway visualization, data integration and functional analysis of signaling pathway using KEGG pathways, molecule pages, biomedical ontology and basic graph theoretic algorithms. It also exports annotated data as BioPAX level3 format for further analysis and data sharing. |
Long Abstract:Click Here |
|
Poster X040 |
Refining the protein interaction network generated by NASCENT using sequence alignment |
Gabor Ivan- PhD Student |
Daniel Banky (PhD student, Dept. of Computer Science); Vince Grolmusz (Professor of mathematics, Dept. of Computer Science); |
Short Abstract: NASCENT is an automatic protein-protein interaction network prediction tool introduced previously. Here we present recent developments of NASCENT: (1) using the whole UniProtKB database as the basis of calculations, and (2) sequence homology-based assignment of source and target proteins. We evaluate and compare the performance of previous and newly added PPI_network generation methods. |
Long Abstract:Click Here |
|
Poster X041 |
Detection of nonlinear effects in gene expression pathways |
Andreas Mayr- Johannes Kepler University Linz |
Djork-Arne Clevert (Johannes Kepler University Linz, Institute of Bioinformatics); Sepp Hochreiter (Johannes Kepler University Linz, Institute of Bioinformatics); |
Short Abstract: Feedback loops and signal cascades lead to genetic pathways with nonlinear dependencies of their expression values. To identify genes of a pathway with nonlinear dependent expression values, we suggest a novel quadratic factor analysis algorithm. Noise and nonlinearities are distinguished by computing p-values for linear genes detected as nonlinear randomly. |
Long Abstract:Click Here |
|
Poster X042 |
Development of a new Physiological Pathways tool at RGD for graphically linking function to genotype and phenotype data |
Diane Munzenmaier- Medical College of Wisconsin |
Melinda Dwinell (Medical College of Wisconsin, Physiology); Mary Shimoyama (Medical College of Wisconsin, Bioinformatics); Weisong Liu (Medical College of Wisconsin, Bioinformatics); Howard Jacob (Medical College of Wisconsin, Physiology); |
Short Abstract: The Rat Genome Database (RGD) has developed a new Physiological Pathways tool which will allow physiologists to easily follow links from known functional mechanisms to relevant phenotype and genotype data. Disease and disorder information as well as data on drug actions and drug-gene interactions will also be included. |
Long Abstract:Click Here |
|
Poster X043 |
Comparison of de novo Network Reverse Engineering Methods with Applications to Ecotoxicology |
Lyle Burgoon- US Environmental Protection Agency |
Stephen Edwards (US EPA, NHEERL); |
Short Abstract: Although the results from the DREAM network construction competitions make clear that methods incorporating other knowledge (e.g., knockouts) fair better in specific circumstances, this additional information are lacking in ecotoxicology studies. Here, we compare the performance of correlation and mutual information network reverse engineering algorithms on synthetic data from AGN. |
Long Abstract:Click Here |
|
Poster X044 |
Using remote structural similarities in protein complex modeling |
Qiangfeng Zhang- Columbia University |
Donald Petrey (Columbia University, Biochemistry and Molecular Biophysics); Raquel Norel (Columbia University, Biochemistry and Molecular Biophysics); Barry Honig (Columbia University, Biochemistry and Molecular Biophysics); |
Short Abstract: We describe a comparative modeling approach to characterize protein-protein complexes. We apply our approach to the analysis of interface conservation, interface prediction, and the computational construction of interactomes that, based on our evaluation, are comparable in quality and reliability to high-throughput experimental approaches. |
Long Abstract:Click Here |
|
Poster X045 |
Interpreting expression data based on large-scale, high-quality literature networks: the Causal Reasoning Engine |
Daniel Ziemek- Pfizer Inc. |
Leonid Chindelevitch (Pfizer Inc., Computational Sciences CoE); Ahmed Enayetallah (Pfizer Inc., Compound Safety Prediction); Savina Jaeger (Pfizer Inc., Computational Sciences CoE); Ranjit Randhawa (Pfizer Inc., Computational Sciences CoE); Ben Sidders (Pfizer Inc., eBiology); Christoph Brockel (Pfizer Inc., Translational & Bioinformatics); Enoch Huang (Pfizer Inc., Computational Sciences CoE); |
Short Abstract: In this work, we demonstrate the use of a large collection of manually curated, directed, and signed relationships among proteins, transcripts, and compounds to detect key drivers of observed differential gene expression. We show preliminary validation of our method for chronic viral infection as well as compound toxicity data-sets. |
Long Abstract:Click Here |
|
Poster X046 |
A new framework for evaluating theoretical models of protein interaction network evolution |
Todd Gibson- University of Colorado Denver |
Debra Goldberg (University of Colorado at Boulder, Computer Science); |
Short Abstract: Models of protein interaction network evolution are built from seed graphs and validated against empirical networks. However, seed graphs can bias the network produced, tainting inferences. We avoid seed graphs by running the model in reverse (and subsequently forward) on both empirical and theoretical networks. |
Long Abstract:Click Here |
|
Poster X047 |
Dissecting the relationship between quantitative genetic and physical interactions and protein complexes |
Yungil Kim- University of Minnesota-Twin Cities |
Anastasia Baryshnikova (University of Toronto, Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research); Michael Costanzo (University of Toronto, Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research); Judice Koh (University of Toronto, Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research); Bryan-Joseph San Luis (University of Toronto, Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research); Sunayan Bandyopadhyay (University of Minnesota-Twin Cities, Department of Computer Science & Engineering); Gary D. Bader (University of Toronto, Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research); Brenda Andrews (University of Toronto, Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research); Charles Boone (University of Toronto, Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research); Chad L. Myers (University of Minnesota-Twin Cities, Department of Computer Science & Engineering); |
Short Abstract: We analyze the relation between protein-protein and genetic interactions based on a network covering 5.4 million double mutants in yeast. We find that positive genetic interactions are more complex than previously appreciated in that they only rarely overlap physical interactions and often span across functionally distant protein complexes (pathways). |
Long Abstract:Click Here |
|
Poster X048 |
A systems biology approach to study drug-induced growth inhibition of Mycobacterium tuberculosis |
Anders Wallqvist- US Army Medical Research & Materiel Command |
Jaques Reifman (US Army Medical Research & Materiel Command, TATRC); Xin Fang (Henry Jackson Foundation, BHSAI); |
Short Abstract: Since metabolism is required for all organisms to survive, interfering with bacterial metabolism is acknowledged as a promising approach to find therapeutics against bacterial infections. In this work, we describe how we have applied ideas in metabolic network modeling, flux-balance analysis, and enzyme kinetics to construct an integrated model of Mycobacterium tuberculosis metabolism and how we use this system to quantitatively study drug dosage and growth inhibition. In particular, we quantitatively reproduced experimentally measured dose-response curves, ranging over three orders of magnitude in inhibitor concentration, for two different inhibitors of metabolic enzymes. |
Long Abstract:Click Here |
|
Poster X049 |
Detection of Mutated Pathways in Cancer |
Fabio Vandin- Brown University |
Fabio Vandin (Brown University, Computer Science); |
Short Abstract: Cancer is driven in part by somatic mutations that accumulate in the genome. Recent whole-genome studies have shown that some known pathways are enriched for somatic mutations, but have not tested whether other less characterized pathways are similarly enriched. We introduce a method for de novo identification of subnetworks of a large-scale gene interaction network that are mutated in a significant number of samples. The significance of a subnetwork is determined by both the frequency that its genes are mutated and the topology of interactions between genes. We also design a two-stage statistical test that rigorously bounds the false discovery rate (FDR) for the number of identified subnetworks while avoiding the severe multiple hypothesis correction required if each individual subnetwork were tested individually. This method extends our earlier work [Vandin, et al. RECOMB 2010] in three directions. First, we use a more realistic statistical model based on the background mutation rate. This results in a more precise and less conservative estimate of the p-value and the FDR for the identified subnetworks. Second, we extend our method to analyze copy number alterations that are common in cancer. Third, we jointly analyze copy number and mutation data. We apply our method to data from The Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and separate study of lung adenocarcinoma. In both datasets, we identify novel subnetworks and subnetworks that significantly overlap known pathways. |
Long Abstract:Click Here |
|
Poster X050 |
Uncovering general principles of regulation in protein interaction networks |
Guilhem Chalancon- MRC Laboratory of Molecular Biology |
No additional authors |
Short Abstract: Despite molecular crowding that constraints protein abundance, how do cells permit protein-protein interactions to span a wide range of affinities? Here, we present evidence that regulation of protein abundance operates differently for interactions involved in complexes, chaperoning and phosphorylation networks. Therefore, stable and transient PPI regulation might be specifically fine-tuned. |
Long Abstract:Click Here |
|
Poster X051 |
Construction of perturbation network among diseases, drugs and miRNAs |
Minjeong Ko- Ewha Research Center for Systems Biology |
Charny Park (Ewha Research Center for Systems Biology, Division of Life and Pharmaceutical Sciences); Hyung-Seok Choi (Ewha Research Center for Systems Biology, Division of Life and Pharmaceutical Sciences); Haeseung Lee (Ewha Research Center for Systems Biology, Division of Life and Pharmaceutical Sciences); Soo-Young Cho (Ewha Research Center for Systems Biology, Division of Life and Pharmaceutical Sciences); Sanghyuk Lee (Ewha Research Center for Systems Biology, Division of Life and Pharmaceutical Sciences); Wan Kyu Kim (Ewha Research Center for Systems Biology, Division of Life and Pharmaceutical Sciences); |
Short Abstract: We aggregated perturbation-related gene expression profiles from GEO(Gene Expression Omnibus) database and performed all-against-all comparisons among different types of perturbations such as disease, drug and miRNA treatment. A number of non-canonical relationships were identified, suggesting novel therapeutic applications of existing drugs and potential roles of miRNAs in several human diseases. |
Long Abstract:Click Here |
|
Poster X052 |
Exploring building principles of metabolic networks with respect to information spread |
Frank Emmert-Streib- Queens University, Belfast |
Galina Glazko (University of Rochester, Computational Biology); Shailesh Tripathi (Queen's University Belfast, Center for Cancer Research); |
Short Abstract: In this study we explore building principles of metabolic networks. We define a new network measure that evaluates the depth of subnetworks, corresponding to pathways, in metabolic networks with respect to information spread. Connecting this measure with evolutionary information about metabolic reactions allows gaining insight in constructional principles. |
Long Abstract:Click Here |
|
Poster X053 |
Network analysis defines the impact of gene-physical activity interactions |
Laurence Parnell- JM-USDA Human Nutrition Research Center on Aging |
Chao-Qiang Lai (JM-USDA Human Nutrition Research Center on Aging, Nutrition and Genomics); Yu-Chi Lee (JM-USDA Human Nutrition Research Center on Aging, Nutrition and Genomics); Jose Ordovas (JM-USDA Human Nutrition Research Center on Aging, Nutrition and Genomics); Roger Fielding (JM-USDA Human Nutrition Research Center on Aging, Nutrition and Exercise Physiology Laboratory); Lakshmanan Iyer (Tufts Medical School, Neuroscience); Eric Wooten (Tufts Medical School, Anatomy and Cellular Biology); |
Short Abstract: Physical activity modulates many genotype-phenotype associations, particularly towards HDL- and LDL-cholesterol. A network of genes harboring PA-sensitive variants and their protein-protein interactors shows an enrichment of functions such as transcription, response to peptide hormone, cell cycle control and response to stress. |
Long Abstract:Click Here |
|
Poster X054 |
Fold change detection and scalar symmetry of sensory input fields |
Oren Shoval- Weizmann Institute of Science |
Lea Goentoro (Harvard Medical School, Systems Biology); Yuval Hart (Weizmann Institute of Science, Molecular Cell Biology); Avi Mayo (Weizmann Institute of Science, Molecular Cell Biology); Eduardo Sontag (Rutgers University, Mathematics); Uri Alon (Weizmann Institute of Science, Molecular Cell Biology); |
Short Abstract: Recent studies suggest that certain cellular sensory systems display fold-change detection (FCD): a response whose entire shape, including amplitude and duration, depends only on fold-changes in input, and not on absolute changes. We discuss benefits of FCD in fields with scalar symmetry, and present a wide class of possible mechanisms. |
Long Abstract:Click Here |
|
Poster X055 |
Inferring the sRNA-mediated post-transcriptional regulatory network in Mycobacterium tuberculosis |
Diogo Veiga- University of Texas M D Anderson Cancer Center |
Paolo Miotto (San Raffaele Scientific Institute, Division of Immunology, Transplantation and Infectious Diseases); Daniela Cirillo (San Raffaele Scientific Institute, Division of Immunology, Transplantation and Infectious Diseases); Marila Gennaro (New Jersey Medical School , ublic Health Research Institute); Gabor Balazsi (University of Texas M D Anderson Cancer Center, Systems Biology); |
Short Abstract: We developed a statistical approach to evaluate the significance of binding predictions for sRNA:mRNA heterodimers. This approach was used to infer a post-transcriptional network in the human pathogen Mycobacterium tuberculosis, where regulatory links represent small non-coding RNAs base-pairing to mRNA targets to modulate their translation. |
Long Abstract:Click Here |
|
Poster X056 |
Modeling the temporal interplay of molecular signaling and gene expression using dynamic nested effects models |
Benedict Anchang- University of Regensburg |
Rainer Spang (University of Regensburg, Institute of functional genomics and bioinformatics); Mohammad Sadeh (University of Regensburg, Institute of functional genomics and bioinformatics); Peter Oefner (University of Regensburg, Institute of functional genomics and bioinformatics); Juby Jacob (University of Regensburg, Institute of functional genomics and bioinformatics); Achim Tresch (University of Munich, Gene Center); |
Short Abstract: DNEM is a mathematical method to investigate the temporal interplay of molecular signaling in a cell.We decoded such a molecular communication in embryonic stem cells of the mouse.The study demonstrated how stem cells carry out differentiation to specialized cells of the body with the help of Nanog, a key regulator. |
Long Abstract:Click Here |
|
Poster X057 |
Reverse pathway analysis of high throughput metabolomics |
Shuzhao Li- Emory University |
Quinlyn Soltow (Emory University, Department of Medicine); Dean Jones (Emory University, Department of Medicine); Bali Pulendran (Emory University, Emory Vaccine Center); |
Short Abstract: A novel method brings the power and contextual information of pathway analysis to untargeted, high throughput metabolomics, demonstrated on human plasma samples in a study of vaccine induced immunity. |
Long Abstract:Click Here |
|
Poster X058 |
bioCompendium: high-throughput experimental data analysis platform |
Venkata Satagopam- EMBL |
Jean-Karim Hériché (EMBL, Cell Biology/Biophysics Unit); Reinhard Schneider (EMBL, Structural and Computational Biology); |
Short Abstract: We have developed a high throughput experimental data analysis platform, 'bioCompendium'. The basic input to the system are one or more gene lists, which are typically the basis for an experiment or are the outcome of a wide range of experiments like gene expression analysis, RNAi screening results, proteomics experiments and 'cross-species comparison' experiments. |
Long Abstract:Click Here |
|
Poster X059 |
“ePlant: integrative systems biology in 3D on the world wide web” |
Geoffrey Fucile- University of Toronto |
David DiBiase (University of Toronto, Cell and Systems Biology); Hardeep Nahal (University of Toronto, Cell and Systems Biology); Lawrence Kelley (Imperial College London, Life Sciences); Nicholas Provart (University of Toronto, Cell and Systems Biology); Dinesh Christendat (University of Toronto, Cell and Systems Biology); |
Short Abstract: "ePlant" (http://bar.utoronto.ca/eplant) is a suite of open-source world wide web-based tools for the interactive integration and 3D visualization of biological data from the kilometer to the nanometer scale. The results of this project include a novel proteome-wide protein structure prediction and annotation for the model plant Arabidopsis thaliana. |
Long Abstract:Click Here |
|
Poster X060 |
An Estimation Method of S-system Model of Gene Regulatory Networks Using Immune Algorithm |
Tomoyoshi Nakayama- Osaka University |
Shigeto Seno (Osaka University, Graduate School of Information Science and Technology); Yoichi Takenaka (Osaka University, Graduate School of Information Science and Technology); Hideo Matsuda (Osaka University, Graduate School of Information Science and Technology); |
Short Abstract: We proposed a method to use the immune algorithm instead of genetic algorithm for estimating genetic networks with S-system model from the time series data of gene expression. The results indicate that our method searches the solution space efficiently and improved estimate accuracy. |
Long Abstract:Click Here |
|
Poster X061 |
GOPHER: A Web-based Tool for Biological Network Analysis Using Gene Expression |
Kory Johnson- NIH/NINDS |
Amar Yavatkar (NIH/NINDS, Bioinformatics & IT Program); Yang Fann (NIH/NINDS, Bioinformatics & IT Program); |
Short Abstract: Web-based tool for ranking KEGG, BioCarta, GenMAPP, and/or GO networks using gene expression for a target set of genes in conjunction with Mahalanobis Distance and/or Hotelling's T-Square. Results using the Fisher Exact Test are supported; as is the ability to cross-compare results with a reference database of results. |
Long Abstract:Click Here |
|
Poster X062 |
Active Pathways: Visualization and analysis of pathways and expression data. |
Joseph Whitney- University of Toronto |
Michael Brudno (University of Toronto, Donnelly Centre for Cellular and Biomolecular Research, Computer Science); Daniele Merico (University of Toronto, Donnelly Centre for Cellular and Biomolecular Research); Gary Bader (University of Toronto, Donnelly Centre for Cellular and Biomolecular Research); |
Short Abstract: The Active Pathways Project aims at providing software tools for the exploration, visualization, and analysis of biomolecular activity data, such as gene expression, in the context of BioPAX-encoded pathways. |
Long Abstract:Click Here |
|
Poster X063 |
ANALYSIS OF THE FUNCTIONAL PROPERTIES OF THE CREATINE KINASE SYSTEM USING A MULTISCALE “SLOPPY” MODELING APPROACH |
Hannes Hettling- VU University Amsterdam |
No additional authors |
Short Abstract: Creatine kinase (CK) has several potential metabolic functions in heart muscle. We combine a computational model of the CK system with multiscale data. Using "Sloppy" ensemble modeling, we define confidence regions on predictions. Results indicate that damping oscillations in energy production and metabolite levels is the primary function of CK. |
Long Abstract:Click Here |
|
Poster X064 |
Metabolic network destruction: relating topology to robustness |
Wynand Winterbach- Delft University of Technology |
Huijuan Wang (Delft University of Technology, Network Architecture and Services); Marcel Reinders (Delft University of Technology, The Delft Bioinformatics Lab); Piet Van Mieghem (Delft University of Technology, Network Architecture and Services); Dick de Ridder (Delft University of Technology, The Delft Bioinformatics Lab); |
Short Abstract: We investigate whether the topology of metabolic networks is related to robustness by "destructing" metabolic networks one reaction at a time and studying correlation between growth and a number of topological metrics.
We find that a few topological metrics correlate well with growth, pointing to the importance of currency metabolites. |
Long Abstract:Click Here |
|
Poster X065 |
Measuring the Temperature of the Yeast Genome: A Parametric Approach to Genomewide Analysis |
Sandy Shaw- Prime Genomics |
No additional authors |
Short Abstract: Analogous to the way that body temperature is used to measure the well-being of the complex system that is the human body, we use recent results in network theory to show that it is possible to parameterize microarray data from S. cerevisiae to take the "temperature" of an entire genome. |
Long Abstract:Click Here |
|
Poster X066 |
Pathway Guided Modules – a method to identify key proteins |
Jieun Jeong- Harvard University |
Andrei Krivtsov (Harvard Medical School, Hematology/Oncology); Scott Armstrong (Harvard Medical School, Hematology/Oncology); Winston Hide (Harvard University, Biostatistics); |
Short Abstract: When two phenotypes are compared using gene expression data, large sets of differentially expressed genes and numerous sets returned by gene set enrichment analysis are hard to interpret. Our tool, PGM (Pathway Guided Modules) identifies the most relevant genes using a "hint" in the form of a major signaling pathway. |
Long Abstract:Click Here |
|
Poster X067 |
A comparative modeling framework to estimate conserved and differential gene-gene interactions |
Zhengyu Ouyang- New Mexico State University |
Thomas J. Ha (University of British Columbia, Department of Medical Genetics); Matt Larouche (University of British Columbia, Department of Medical Genetics); Mingzhou Song (New Mexico State University, Computer Science Department); |
Short Abstract: A comparative modeling framework is proposed to estimate conserved and differential gene-gene interactions from time course observations under two conditions. By evaluating the heterogeneity and homogeneity of a pair of interactions for each gene, a decision on differential or conserved interactions for each gene is made. |
Long Abstract:Click Here |
|
Poster X068 |
Analyzing protein interaction networks using structural information. |
Anne Campagna- CRG |
Christina Kiel (CRG, EMBL-CRG Systems Biology Unit); Luis Serrano (CRG, EMBL-CRG Systems Biology Unit); |
Short Abstract: The knowledge of interfaces provided by structural data can add a dynamic value to the classical static picture of networks. We developed and applied such a methodology on the Rhodopsin signaling pathway, which permitted describing the compatible/exclusive interactions occurring either in the dark-inactivated or in the light-activated state of Rhodopsin. |
Long Abstract:Click Here |
|
Poster X069 |
Ranking Disease Genes Based on Connections in a Biological Network |
Chia-Lang Hsu- National Yang-Ming University |
Ueng-Cheng Yang (National Yang-Ming University, Center for Systems and Synthetic Biology); |
Short Abstract: We have developed a parameter-free and network-based method for ranking human disease candidate genes. This method is based on a local topological property between candidate genes and related disease-causing genes in biological networks. |
Long Abstract:Click Here |
|
Poster X070 |
Systematic Identification of YY1/miRNAs Mediated Regulatory Networks in Skeletal Myogenesis |
Hao Sun- The Chinese University of Hong Kong |
xiaoxi Su (The Chinese University of Hong Kong, Department of Chemical Pathology); Zhang Chen (The Chinese University of Hong Kong, Department of Chemical Pathology); Leina Lu (The Chinese University of Hong Kong, Department of Chemical Pathology); Peiyong Jiang (The Chinese University of Hong Kong, Department of Chemical Pathology); Huating Wang (The Chinese University of Hong Kong, Department of Obstetrics and Gynaecology ); |
Short Abstract: YY1 and miRNAs are important regulators. They play important roles in controlling gene expression during skeletal myogenesis. By combining computational method with high-throughput genomic data, we constructed a novel YY1/miRNAs mediated gene regulatory network. Our results demonstrated this approach could provide novel insights into transcription factor/miRNAs meditated gene regulatory networks. |
Long Abstract:Click Here |
|
Poster X071 |
Inferring regulatory network structures for canonical pathways from gene expression data |
Diogo Camacho- HHMI / BU |
James Costello (HHMI / BU, Biomedical Engineering); James Collins (HHMI / BU, Biomedical Engineering); |
Short Abstract: Here we present a method that takes advantage of existing genome-scale datasets to directly identify regulatory interactions between any given regulator and specific cellular pathways. We will demonstrate that it provides biologically meaningful results and outperforms methods that infer regulator-to-process relationships from gene-to-gene networks. |
Long Abstract:Click Here |
|
Poster X072 |
Time-dependent key element analysis with active state transition diagram: a high-level Petri net approach |
Chen Li- University of Tokyo |
Masao Nagasaki (University of Tokyo, Human Genome Center, Institute of Medical Science); Ayumu Saito (University of Tokyo, Human Genome Center, Institute of Medical Science); Satoru Miyano (University of Tokyo, Human Genome Center, Institute of Medical Science); |
Short Abstract: We develop a method to build a framework for automatically constructing an active state transition diagram for dynamic analysis regarding structural changes over time in a high-level Petri net model. The method produces simplified graphical representation about temporal information, which is demonstrated by using circadian rhythm model in Drosophila. |
Long Abstract:Click Here |
|
Poster X073 |
Protein Evolution in Yeast Transcription Factor Subnetworks |
Eric Franzosa- Boston University |
Yong Wang (Chinese Academy of Sciences, Academy of Mathematics and Systems Science); Xiang-Sun Zhang (Chinese Academy of Sciences, Academy of Mathematics and Systems Science); Yu Xia (Boston University, Bioinformatics Program, Department of Chemistry, Department of Biomedical Engineering); |
Short Abstract: We introduce the idea of transcription factor (TF) subnetworks within the global yeast protein-protein interaction and transcriptional regulatory networks. We demonstrate that, unlike global protein hubs, TF hubs in these subnetworks do not tend to evolve significantly more slowly than TF non-hubs, and in some cases evolve significantly faster. |
Long Abstract:Click Here |
|
Poster X074 |
Prediction of transcriptional regulatory networks in soybean pathogen defense responses using a module networks procedure |
Whitham SA- Iowa State University |
No additional authors |
Short Abstract: We are using the soybean-Asian soybean rust (ASR) pathosystem as a model to understand crop defense gene networks. Time course expression profiling data collected from multiple-experiments involving resistant and susceptible soybean genotypes was combined with heterogeneous annotation data for integration into a module network approach. |
Long Abstract:Click Here |
|
Poster X075 |
Predicting Functionally Important Co-evolving Secondary Structures from Mutation Events using Mutual Information and Network Theory |
Scooter Willis- The Scripps Research Institute |
Jun Zhang (The Scripps Research Institute, Department of Molecular Therapeutics); Patrick Griffin (The Scripps Research Institute, Department of Molecular Therapeutics); |
Short Abstract: Mutual information can be used to detect co-evolving amino acids in a multiple sequence alignment where the sum of information between secondary structures can indicate they are co-evolving. We minimize the phylogenetic influences on sequence entropy by sampling mutation events from a consensus phylogenetic tree when calculating mutual information. |
Long Abstract:Click Here |
|
Poster X076 |
twzPEA: A topology and working-zone based pathway enrichment analysis framework |
Yang Zhang- New Mexico State University |
Joe Song (New Mexico State University, Computer Science); Z.Lewis Liu (Department of Agriculture, Agricultural Research Service); |
Short Abstract: We developed an effective topology and working-zone based pathway enrichment analysis framework to determine the involvement of a general pathway in comparative experimental conditions. The differential involvement of the pentose pathway detected by applying our program to two yeast datasets agreed with an independent study using qRT-PCR. |
Long Abstract:Click Here |
|
Poster X077 |
A Boolean Network Model of Nuclear Receptor Mediated Cell Cycle Progression |
John Jack- Environmental Protection Agency (EPA) |
Christopher Haugh (Environmental Protection Agency (EPA), National Center for Computational Toxicology (NCCT)); John Wambaugh (Environmental Protection Agency (EPA), National Center for Computational Toxicology (NCCT)); Imran Shah (Environmental Protection Agency (EPA), National Center for Computational Toxicology (NCCT)); |
Short Abstract: We developed a Boolean Network model to simulate nuclear receptor mediated interactions with growth factor crosstalk pathways. The model explains some of the experimental evidence on the impact of nuclear receptor activation on hepatocyte proliferation, and can be useful for evaluating the mitogenic effects of chemicals using in vitro data. |
Long Abstract:Click Here |
|
Poster X078 |
Finding optimal subgraph measures for protein complexes |
Debra Goldberg- University of Colorado |
Daniel Houck (University of Colorado, Computer Science); Suzanne Gallagher (University of Colorado, Computer Science); |
Short Abstract: We investigate graph statistics in complexes in protein interaction networks. For each complex, we want the "optimal" subgraph for each measure, the subgraph most likely to be found based on that measure. Finding these optimal subgraphs in known complexes could determine what thresholds should be used in complex-finding algorithms. |
Long Abstract:Click Here |
|
Poster X079 |
Comparative Interactions between Wild Type and Transgenic Alfalfa |
Travis Cotton- New Mexico State University |
Zhengyu Ouyang (New Mexico State University, Computer Science); Joe Song (New Mexico State University, Computer Science); Omar Holguin (New Mexico State University, Plant and Environmental Sciences); Champa Sengupta-Gopalan (New Mexico State University, Plant and Environmental Sciences); |
Short Abstract: We compare wild type and transgenic alfalfa metabolic data using linear clustering and ordinary differential equations (ODE's). The objective is to discover metabolites which may cause a differential interaction in the metabolic pathways of alfalfa. Using a Dynamical System Model (DSM) and linear clustering, we discover which pathways changed from wild-type to transgenic alfalfa. |
Long Abstract:Click Here |
|
Poster X080 |
A platform for computation of microbial ecosystems in time and space: COMETS |
William Riehl- Boston University |
Christopher Marx (Harvard University, Department of Organismic and Evolutionary Biology); Nathaniel Cady (University at Albany, College of Nanoscale Science and Engineering); Daniel Segre (Boston University, Program in Bioinformatics, Departments of Biology and Biomedical Engineering); |
Short Abstract: We present a platform for Computation Of Microbial Ecosystems in Time and Space (COMETS). This uses dynamic flux balance analysis on a lattice to simulate spatio-temporal growth patterns of multiple species. We explore possible applications of COMETS in predicting the effects of media, geography, or species diversity on microbial ecosystems. |
Long Abstract:Click Here |
|
Poster X081 |
Predicting Gene Function from Interaction Networks |
Sara Mostafavi- University of Toronto |
Quaid Morris (University of Toronto, Banting and Best Institute, Department of Computer Science); Anna Goldenberg (University of Toronto, Centre for Cellular and Biomoleular Research); |
Short Abstract: We propose a new algorithm for predicting gene function from molecular interaction network that considers weighted sums of path lengths between genes when making predictions. Our algorithm is both faster and more accurate than the state-of-art label propagation algorithms on a variety of interaction networks and functions. |
Long Abstract:Click Here |
|
Poster X082 |
Unsupervised detection of lung cancer biomarkers using biological networks |
Anna Goldenberg- University of Toronto |
Sara Mostafavi (University of Toronto, Computer Science); Gerald Quon (University of Toronto, Computer Science); Paul Boutros (Ontario Institute for Cancer, ); Quaid Morris (University of Toronto, Best and Banting Dept for Biomedical Research); |
Short Abstract: Our unsupervised approach identifies genes that, in the context of a biological network, can explain the changes of expression across remaining genes. Applied to lung cancer data, our framework recovers a small number of genes (previously associated with cancer) that account for most of the expression changes in tumor samples. |
Long Abstract:Click Here |
|
Poster X083 |
Association Analysis Approach For Finding Coherent Value Bicliques In Genetic Interaction Data |
Gowtham Atluri- University of Minnesota |
Jeremy Bellay (University of Minnesota, Computer Science); Gaurav Pandey (University of Minnesota, Computer Science); Chad Myers (University of Minnesota, Computer Science); Vipin Kumar (University of Minnesota, Computer Science); |
Short Abstract: This poster presents an association analysis based approach for efficient and exhaustive discovery of coherent value bicliques from a yeast quantitative genetic interaction data. These bicliques represent parallel pathways in a yeast cell. Our technique is able to exhaustively find all such bicliques, that other competing approaches cannot discover. |
Long Abstract:Click Here |
|
Poster X084 |
Modelling cellular embryonic development with spatiotemporal patterns of gene expressions |
Hien Nguyen- New Mexico State University |
Joe Song (New Mexico State University, Computer Science); |
Short Abstract: We integrate a cell-based model to the gene expression level model. Our integrated model is able to reveal the relationship between the cell behaviors and the protein concentration during the embryonic development. It's also able to predict some of the pattern formations in cellular scale of the embryos. |
Long Abstract:Click Here |
|
Poster X085 |
Differential regulatory roles of SOX2 in embryonic and neural stem cells |
Jihae Seo- Korea Research Institute of Bioscience & Biotechnology (KRIBB) |
Jihyun Lee (College of Pharmacy, Seoul National University, Information Center for Bio-pharmacological Network(i-Pharm)); Sanghyuk Lee* (Korea Research Institute of Bioscience & Biotechnology (KRIBB), Korean Bioinformation Center (KOBIC) ); |
Short Abstract: We have analyzed the differential regulatory roles of SOX2 in two different stem cells (ESC and NSC) based on ChIP-chip experiment. Pattern and gene set analyses showed distinctive binding motifs and functions, respectively. We also constructed the transcription regulation networks for each cell type. |
Long Abstract:Click Here |
|
Poster X086 |
Omics Integration and Analysis |
Erin Boggess- Iowa State University |
No additional authors |
Short Abstract: Statistical analyses of interrelated omics datasets are traditionally carried out independently of one another. When omics experiments are performed for common samples, simultaneous data analysis is possible. Advantages of a global analysis include identification of interactions within and across cellular functional layers and more comprehensive classification schemes. |
Long Abstract:Click Here |
|
Poster X087 |
Genome-Wide Association Data Reveal a Global Map of Genetic Interactions among Protein Complexes |
Rohith Srivas- University of California, San Diego |
Gregory Hannum (University of California, San Diego, Bioengineering); Aude Guénolé (Leiden University Medical Center, Toxicogenetics); Haico van Attikum (Leiden University Medical Center, Toxicogenetics); Nevan Krogan (University of California, San Francisco, Cellular and Molecular Pharmacology); Richard Karp (University of California, Berkeley, Electrical Engineering and Computer Science); Trey Ideker (University of California, San Diego, Bioengineering and Medicine); |
Short Abstract: Despite the immense potential of gene association studies, they have been challenging to analyze because most traits are complex, involving the combined effect of mutations at many different genes. Due to lack of statistical power, only the strongest single markers are typically identified. Here, we present an integrative approach that greatly increases power through marker clustering and projection of marker interactions within and across protein complexes. Applied to a recent gene association study in yeast, this approach identifies 2,023 genetic interactions which map to 208 functional interactions among protein complexes. We show that such interactions are analogous to interactions derived through reverse genetic screens and that they provide coverage in areas not yet tested by reverse genetic analysis. As proof of principle, we use synthetic genetic screens to confirm numerous novel genetic interactions for the INO80 chromatin remodeling complex. |
Long Abstract:Click Here |
|
Poster X088 |
Exploring Disease Interactions Using Combined Gene and Phenotype Networks |
Nitesh Chawla- University of Notre Dame |
Darcy Davis (University of Notre Dame, Computer Science & Engg.); Nitesh Chawla (University of Notre Dame, Computer Science & Engg.); |
Short Abstract: Faced by unsustainable costs and enormous amounts of under-utilized data, health care needs more efficient practices, research, and tools to harness the benefits of data. These methods should create a feedback loop where computational tools guide and facilitate research, leading to improved biological knowledge and clinical standards, which in turn should generate better data. We build and analyzing disease interaction networks based on data collected from previous genetic association studies and patient medical histories, spanning over 12 years, acquired from a regional hospital. By exploring both individual and combined interactions among these two levels of disease data, we provide in- sight into the interplay between genetics and clinical realities. Our results show a marked difference between the well-defined structure of genetic relationships and the chaotic co-morbidity network, but also highlight clear interdependencies. Additionally, we use significant patterns in the data to locate good target sites for further association research. |
Long Abstract:Click Here |
|
Poster X089 |
Modeling information flow from metabolomics to transcriptomics |
Xinghua Lu- Medical Univ SC |
L. Ashley Cowart (Medical Univ SC, Biochemistry); Adam Richards (Medical Univ SC, Biochemistry); David Montefusco (Medical Univ SC, Biochemistry); Yusuf Hannun (Medical Univ SC, Biochemistry); Matthews Shotwell (Medical Univ SC, Biochemistry); |
Short Abstract: We present a systems-biology approach recently developed to model the information flow from cellular stress to metabolic changes then further to transcriptomic changes. We studied a yeast system in which cellular stresses cause changed metabolism of a family of bioactive lipids referred to as sphingolipids, which further mediate gene expression in response to cellular stress. We have developed a statistical framework which, through integrating multiple types of high throughput data, infers the activation states of transcription factors (TFs), reveals the information (connectivity) between lipidomic and transcriptomic data, and models activation of TFs by specific lipids. Our model led to a testable hypothesis revealing a signal transduction pathway involving phytospingosine-1-phosphate to HAP complex and then to genes involved in energy metabolism. The hypothesis was experimentally validated. |
Long Abstract:Click Here |
|
Poster X090 |
A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection |
Irit Gat-Viks- Broad Institute |
Sagi Shapira (Broad Institute, biology); Bennett Shum (Broad Institute, biology); Amelie Dricot, Dricot (Massachusetts General Hospital, Genetics); Marciela de Grace (Broad Institute, Biology); Liguo Wu (Broad Institute, Biology); Piyush Gupta (Broad Institute, Biology); Serena Silver (Broad Institute, Biology); David Root (Broad Institute, Biology); Aviv Regev (Broad Institute, Biology); Nir Hacohen (Broad Institute, Biology); Tong Hao (Massachusetts General Hospital-, Genetics); David Hill (Massachusetts General Hospital-, Genetics); |
Short Abstract: We combine assays for physical interactions, transcriptional profiling and functional screens to construct a physical and regulatory model of influenza-human interactions during infection. The resulting model implicates a novel role for many host components during influenza infection. Our study establishes a general strategy for uncovering complex host-pathogen relationships. |
Long Abstract:Click Here |
|
Poster X091 |
Cell-specific information processing in segregating populations of Eph receptor ephrin-expressing cells. |
Claus Jørgensen- ICR |
No additional authors |
Short Abstract: Here we presented the first quantitative network model of cell specific Eph receptor-ephrin signal processing following cell to cell contact. Cell specific phospho-tyrosine signaling was measured between Eph- and ephrin-expressing cells using a novel quantitative mass spectrometric approach, which subsequently was integrated with phenotypic data of receptor-ligand function, obtained by siRNA screening. Through data-driven computational modeling, cell specific networks were constructed to describe quantitative signaling trajectories from kinases to effecter modules. Comparative analysis between receptor and ligand expressing cells revealed cell specific differences in the network utilization, in part explained by differences in kinase activity. Network analysis of signaling trajectories between wild-type and signaling impaired Eph-ephrin mutants provided insight into underlying differences in the networks controlling cell specific behavior, and suggested non-cell autonomous effects of both the Eph receptor and the ephrin. Finally, we show that signaling trajectories were utilized significantly different dependent on the context. |
Long Abstract:Click Here |
|
Poster X092 |
Cooperativity within proximal phosphorylation sites is revealed from large-scale proteomics data |
Regev Schweiger- The Hebrew University of Jerusalem |
Michal Linial (The Hebrew University of Jerusalem, Institute of Life Sciences); |
Short Abstract: Phosphorylation is the most prevalent post-translational modification of eukaryotic proteins. Multisite phosphorylation enables a specific combination of phosphosites to determine the speed, specificity and duration of biological responses. We have statistically analyzed a collection of ~70,000 reported phosphosites from in-vivo experiments. We show that phosphosites tend to cluster along the proteins as dense clusters of Serine/Threonines (pS/pT) and between Serine/Threonines and Tyrosines, but generally not between Tyrosines (pY). Our results show that these patterns are valid to all organisms and that it is far more ubiquitous than previously anticipated.
We present evidence supporting the notion that such clusters should be considered as the elementary building blocks in phosphorylation regulation, and that they may increase the robustness of the effectiveness of phosphorylation-dependent responses. These general observations may determine the speed, specificity and duration of a biological responses. |
Long Abstract:Click Here |
|
Poster X093 |
Automaton Based Analysis Of Affected Cells in a 3-Dim Coordinated SYstem |
jitesh dundas- Edencore Technologies |
No additional authors |
Short Abstract: The aim of this research review is to propose the logic and search mechanism for the development of an artificially intelligent automaton (AIA) that can find affected cells in a 3- dimensional biological system. The focus of this paper is on the possible application of this automaton to detect and control cancer cells in the human body. |
Long Abstract:Click Here |
|
Poster X094 |
Macroscopic Kinetic Effect of Cell-to-Cell Variation in Biochemical Reactions |
Nathan Price- University of Illinois |
Pan-Jun Kim (University of Illinois, Institute for Genomic Biology); |
Short Abstract: Genetically identical cells can show strong variations in protein copy numbers due to inherent stochasticity in individual cells. We found that variations in enzyme abundance cause metabolic Kinetic parameters at the cell population level to be significantly deviated from those of single cells, and can even destroy the Michaelis-Menten kinetics. |
Long Abstract:Click Here |
|
Poster X095 |
Topological network alignment uncovers biological function, phylogeny, and disease |
Natasa Przulj- Imperial College London |
Tijana Milenkovic (University of California Irvine, Computer Science); Oleksii Kuchaiev (University of California Irvine, Computer Science); Vesna Memisevic (University of California Irvine, Computer Science); Wayne Hayes (University of California Irvine, Computer Science); Weng L. Ng (University of California, Irvine, Computer Science); |
Short Abstract: There are tens of thousands of genes in the human genome, all encoded in our DNA. Sequence alignment tools have led to many advances in our understanding of evolution, biology, and disease. However, genes are just a means to an end: they produce thousands of different protein types that interact in complex networked ways and make our cells work and keep us alive. We developed novel methods for systematically studying and aligning biological networks that are likely to teach us much about biology, evolution, and disease. In particular, we demonstrate that species phylogeny, as well as detailed biological function of proteins and their involvement in disease-related pathways can be extracted from our purely topology-based alignments. Hence, topology-based analyses and alignments have the potential to provide a completely new, independent source of biological and phylogenetic information. Furthermore, we demonstrate that sequence and topology give insights into complementary slices of biological information. |
Long Abstract:Click Here |
|
Poster X096 |
OpenFreezer LARISA: A web-based laboratory information and workflow management system |
Marina Olhovsky- Samuel Lunenfeld Research Institute, Mount Sinai Hospital |
Marina Olhovsky (Mount Sinai Hospital, SLRI); |
Short Abstract: With the creation of large-scale reagent collections and high-throughput approaches that utilize them, there is a strong demand among biological researchers for a central content management system to ensure consistent data storage, easy retrieval, and user-friendly output. We present OpenFreezer LARISA, a web-based LIMS that provides mechanisms for uniform organization of reagents and experimental results, as well as data analysis and workflow automation tools. OpenFreezer LARISA enables large-scale studies while being versatile enough to cater to smaller projects. At the core of the system is a repository of reagents available within the laboratory and an interface for viewing virtual representations of these reagents. Latest version of OpenFreezer LARISA supports addition of new reagent types by allowing users to define a set of custom properties characterizing reagents of this type; these properties may include DNA, Protein or RNA sequence, features such as polyA tail, tag, intron, or cleavage site, as well as nomenclature and cross-database identifiers (accession number, official gene symbol, Entrez gene ID, Ensembl transcript ID), or other property types matching the user's requirements. Included in OpenFreezer LARISA is a set of computational tools that aid in cloning cDNAs. The scalability, portability, robustness and flexibility of this LIMS software makes it an attractive tool for use by biological laboratories. We are now in preparation to release OpenFreezer LARISA as an OpenSource project in the summer and we encourage the community to join in the development. |
Long Abstract:Click Here |
|
Poster X097 |
Comparative Network Analysis of Complex Diseases |
Rune Linding- The Institute of Cancer Research (ICR) |
No additional authors |
Short Abstract: Insights into the evolution of protein phosphorylation were revealed by combining the results from two computational analyses—a sequence-alignment approach and a kinase-substrate network alignment approach. The two approaches yielded different, but somewhat overlapping, sets of conserved phosphoproteins among humans and the model organisms. The first provided a set of genes encoding phosphoproteins that had positionally conserved phosphorylation sites, whereas the second included many functionally conserved phosphoproteins that lacked this positional conservation. Enrichment analysis of the genes identified through the kinase-substrate network approach suggested that genes encoding phosphorylated signaling hubs were enriched in disease-associated genes. Our analysis also suggests that conserved regulatory networks may be involved in different diseases. These findings may produce new targets for therapeutic intervention or permit researchers to predict the best combinations of therapeutics for intervening in diseases associated with aberrant signaling networks. |
Long Abstract:Click Here |
|
Poster X098 |
Characterization of Primary Breast Tumors based on miRNA-mRNA Integrated Analysis |
Israel Steinfeld- Technion Israel Institute of Technology |
No additional authors |
Short Abstract: Deregulation of micro-RNAs (miRNAs) has been increasingly implicated in cancer. Several miRNAs have aberrant expression profiles in breast cancer and the expression of some has been correlated to specific clinical features of breast cancer. miRNA dependent regulation is mediated through changes in mRNA levels and function, and miRNA/mRNA interaction in the context of breast cancer highlights clinically relevant pathways. In this study we present and analyze data derived from expression profiling of 799 miRNAs in 101 primary human breast tumors, along with genome-wide mRNA profiles and extensive clinical information. We investigate the relationship between these molecular components, in terms of their effect on clinical characteristics and cellular processes. We identify statistically significant differential expression of miRNAs between molecular intrinsic subtypes, and between samples with different levels of proliferation. We introduce a systems biology approach to examine the correlative relationship between miRNA and mRNAs using statistical enrichment methods and generate a miRNA-GO association network. We show that several cellular processes, such as proliferation, cell adhesion and immune response, are strongly associated with certain miRNAs. For example, we observe a strong association of miR-17/92 family/cluster with proliferation. We validate the role of miRNAs in regulating proliferation using high-throughput lysate-microarrays on cell lines including a direct effect of miR-19a. This study provides a comprehensive dataset as well as methods and system-level results that jointly form a basis for further work on understanding the role of miRNA in primary breast cancer. |
Long Abstract:Click Here |
|
Poster X099 |
Deriving Kinase-Substrate Network Models from Co-Modulated Phosphorylation Events. |
Erwin Schoof- The Institute of Cancer Research |
Rune Linding (P.I., Cellular and Molecular Logic Team); |
Short Abstract: Phosphorylation governs cellular information processing, but determining which kinases phosphorylate cellular substrates remains challenging. We can enhance computational models such as NetworKIN by integrating linear motif-based interactions from modular domains such as SH2 or PTB, and by correlating phosphorylation states of kinase activation loops to phosphorylation dynamics on predicted substrates. |
Long Abstract:Click Here |
|
Poster X100 |
Cloud based scientific workflow for NMR data analysis |
Ashwin Manjunatha- Wright State University |
Paul Anderson (Wright State University, Computer Science & Engineering ); Satya Sahoo (Wright State University, Computer Science & Engineering ); Michael Raymer (Wright State University, Computer Science ); Amit Sheth (Wright State University, Computer Science); Ajith Ranabahu (Wright State University, Computer Science); |
Short Abstract: Metabolomics requires computationally intensive analysis of large scale spectroscopic data sets. Selecting the analysis workflow is experiment dependent, thus, an analysis platform must be robust and flexible. We present a scalable scientific workflow approach to data analysis, where the individual cloud-based services exploit the inherent parallel structure of the algorithms. |
Long Abstract:Click Here |
|
Accepted Posters
↑ TOP
|